import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A						=			logging.get_logger(__name__)

A						=			{
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   'encodec'
		def __init__(       self     :     int     ,  snake_case__     :     Dict=[1.5, 3.0, 6.0, 12.0, 24.0]     ,  snake_case__     :     List[str]=2_4_0_0_0     ,  snake_case__     :     int=1     ,  snake_case__     :     int=False     ,  snake_case__     :     str=None     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     Any=1_2_8     ,  snake_case__     :     Optional[int]=3_2     ,  snake_case__     :     Union[str, Any]=1     ,  snake_case__     :     Optional[int]=[8, 5, 4, 2]     ,  snake_case__     :     Any="weight_norm"     ,  snake_case__     :     Any=7     ,  snake_case__     :     List[Any]=7     ,  snake_case__     :     int=3     ,  snake_case__     :     int=2     ,  snake_case__     :     Optional[Any]=True     ,  snake_case__     :     Optional[Any]="reflect"     ,  snake_case__     :     Union[str, Any]=2     ,  snake_case__     :     Optional[Any]=2     ,  snake_case__     :     Tuple=1.0     ,  snake_case__     :     Optional[int]=1_0_2_4     ,  snake_case__     :     Any=None     ,  snake_case__     :     int=True     ,  **snake_case__     :     Optional[Any]     ,  )      ->	Union[str, Any]:
				_lowerCamelCase				   =  target_bandwidths
				_lowerCamelCase				   =  sampling_rate
				_lowerCamelCase				   =  audio_channels
				_lowerCamelCase				   =  normalize
				_lowerCamelCase				   =  chunk_length_s
				_lowerCamelCase				   =  overlap
				_lowerCamelCase				   =  hidden_size
				_lowerCamelCase				   =  num_filters
				_lowerCamelCase				   =  num_residual_layers
				_lowerCamelCase				   =  upsampling_ratios
				_lowerCamelCase				   =  norm_type
				_lowerCamelCase				   =  kernel_size
				_lowerCamelCase				   =  last_kernel_size
				_lowerCamelCase				   =  residual_kernel_size
				_lowerCamelCase				   =  dilation_growth_rate
				_lowerCamelCase				   =  use_causal_conv
				_lowerCamelCase				   =  pad_mode
				_lowerCamelCase				   =  compress
				_lowerCamelCase				   =  num_lstm_layers
				_lowerCamelCase				   =  trim_right_ratio
				_lowerCamelCase				   =  codebook_size
				_lowerCamelCase				   =  codebook_dim if codebook_dim is not None else hidden_size
				_lowerCamelCase				   =  use_conv_shortcut

				if self.norm_type not in ["weight_norm", "time_group_norm"]:
						raise ValueError(
						    f"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""		)

				super().__init__(**snake_case__		)
		@property
		def   _snake_case       (       self     :     int		)      ->	Optional[int]:
				if self.chunk_length_s is None:
						return None
				else:
						return int(self.chunk_length_s * self.sampling_rate		)
		@property
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Optional[int]:
				if self.chunk_length_s is None or self.overlap is None:
						return None
				else:
						return max(1     ,  int((1.0 - self.overlap) * self.chunk_length		)		)
		@property
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	int:
				_lowerCamelCase				   =  np.prod(self.upsampling_ratios		)
				return math.ceil(self.sampling_rate / hop_length		)



		@property
		def   _snake_case       (       self     :     Optional[int]		)      ->	int:
				return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)		)
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return math.sqrt(sum(pow(a - b			,       2							) for a, b in zip(UpperCamelCase			,       UpperCamelCase							)							)							)


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    list[list[list[float] | float]]:

		if dataset.ndim != value_array.ndim:
				_lowerCamelCase				   =  (
				    'Wrong input data\'s dimensions... '
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(UpperCamelCase							)

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_lowerCamelCase				   =  (
						    'Wrong input data\'s shape... '
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(UpperCamelCase							)
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError('Wrong shape'							)

		if dataset.dtype != value_array.dtype:
				_lowerCamelCase				   =  (
				    'Input data have different datatype... '
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(UpperCamelCase							)

		_lowerCamelCase				   =  []

		for value in value_array:
				_lowerCamelCase				   =  euclidean(UpperCamelCase			,       dataset[0]							)
				_lowerCamelCase				   =  dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_lowerCamelCase				   =  euclidean(UpperCamelCase			,       UpperCamelCase							)

						if dist > temp_dist:
								_lowerCamelCase				   =  temp_dist
								_lowerCamelCase				   =  dataset_value.tolist()

				answer.append([vector, dist]							)

		return answer


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return np.dot(UpperCamelCase			,       UpperCamelCase							) / (norm(UpperCamelCase							) * norm(UpperCamelCase							))


if __name__ == "__main__":
				import doctest

				doctest.testmod()